Empirical techniques are developed to adjust dynamic model forecasts on the seasonal time scale for southern African summer rainfall. The techniques, called perfect prognosis and model output statistics (MOS), are utilized to statistically "recalibrate" general circulation model (GCM) large-scale fields to three equi-probable rainfall categories for December to February. The recalibration is applied to a GCM experiment where simultaneously observed sea-surface temperature (SST) fields serve as the lower boundary forcing, referred to as the simulation mode experiment. Cross-validation sensitivity tests are first performed over a 28-year climate period to design an optimal canonical correlation analysis (CCA) model for each of the two recalibration methods. After considering several potential predictor fields, the 700 hPa geopotential height field is selected as the single predictor field in the two sets of statistical equations that are subsequently used to produce recalibrated rainfall simulations over a 1 a-year independent test period. Patterns analysis of the predictor and predictand fields suggests that anomalously low (high) 700 hPa geopotential heights over the subcontinent are associated with wet (dry) conditions over land, an association that is supported by observational evidence of rain (drought) producing systems. Additionally, the dominant mode of the recalibration equations is associated with the EI Nino/Southern Oscillation (ENSO) phenomenon. Somewhat higher retro-active skill levels are found using the MOS technique, but the computationally less intensive perfect prognosis technique should also be able to produce usable seasonal rainfall forecasts over southern Africa in an operational forecast environment hampered by the lack of computing resources. / Dissertation (MSc)--University of Pretoria, 2006. / Geography, Geoinformatics and Meteorology / Unrestricted

The last decade has seen a surge in the development of Brain-Machine Interfaces (BMI) as assistive neural devices for paralysis patients. Current BMI research typically involves a subject performing movements by controlling a robotic prosthesis. The neural signal that we consider for analysis is the Local Field Potential (LFP). The LFP is a low frequency neural signal recorded from intra-cortical electrodes, and has been recognized as one containing movement information. This thesis investigates hand-movement prediction using LFP data as input. In Chapter 1, we give an overview of Brain Machine Interfaces. In Chapter 2, we review the necessary concepts in time series analysis and pattern recognition. In the final chapter, we discuss classification accuracies when considering Summed power and Coherence as feature vectors.

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A co-location pattern is a set of spatial features frequently located together in space. A frequent pattern is a set of items that frequently appears in a transaction database. Since its introduction, the paradigm of frequent pattern mining has undergone a shift from candidate generation-and-test based approaches to projection based approaches. Co-location patterns resemble frequent patterns in many aspects. However, the lack of transaction concept, which is crucial in frequent pattern mining, makes the similar shift of paradigm in co-location pattern mining very difficult. This thesis investigates a projection based co-location pattern mining paradigm. In particular, a FP-tree based co-location mining framework and an algorithm called FP-CM, for FP-tree based co-location miner, are proposed. It is proved that FP-CM is complete, correct, and only requires a small constant number of database scans. The experimental results show that FP-CM outperforms candidate generation-and-test based co-location miner by an order of magnitude.

In this dissertation the problem of recognising vehicle licence plates of which the sym¬bols can not be segmented by standard image processing techniques is addressed. Most licence plate recognition systems proposed in the literature do not compensate for dis¬torted, obscured and damaged licence plates. We implemented a novel system which uses a neural network/ hidden Markov model hybrid for licence plate recognition. We implemented a region growing algorithm, which was shown to work well when used to extract the licence plate from a vehicle image. Our vertical edges algorithm was not as successful. We also used the region growing algorithm to separate the symbols in the licence plate. Where the region growing algorithm failed, possible symbol borders were identified by calculating local minima of a vertical projection of the region. A multilayer perceptron neural network was used to estimate symbol probabilities of all the possible symbols in the region. The licence plate symbols were the inputs of the neural network, and were scaled to a constant size. We found that 7 x 12 gave the best character recognition rate. Out of 2117 licence plate symbols we achieved a symbol recognition rate of 99.53%. By using the vertical projection of a licence plate image, we were able to separate the licence plate symbols out of images for which the region growing algorithm failed. Legal licence plate sequences were used to construct a hidden Markov model contain¬ing all allowed symbol orderings. By adapting the Viterbi algorithm with sequencing constraints, the most likely licence plate symbol sequences were calculated, along with a confidence measure. The confidence measure enabled us to use more than one licence plate and symbol segmentation technique. Our recognition rate increased dramatically when we com¬bined the different techniques. The results obtained showed that the system developed worked well, and achieved a licence plate recognition rate of 93.7%. / Dissertation (MEng (Computer Engineering))--University of Pretoria, 2002. / Electrical, Electronic and Computer Engineering / unrestricted

Thesis: M.S., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 1976 / Bibliography: leaves 123-125. / M.S. / M.S. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science

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The design implications of chaos theory are explored. What does this theory mean, if anything, to landscape architecture or architecture? In order to investigate these questions, the research was divided into four components relevant to design. First, philosophical- chaos offers a nonlinear understanding about place and nature. Second, aesthetical-fractals describe a deep beauty and order in nature. Thirdly, modeling-it is a qualitative method of modeling natural processes. Lastly, managing- concepts of chaos theory can be exploited to mimic processes found in nature. These components draw from applications and selected literature of chaos theory. From these research components, design implications were organized and concluded. Philosophical implications, offer a different, nonlinear realization about nature for designers. Aesthetic conclusions, argue that fractal geometry can articulate an innate beauty (a scaling phenomenon) in nature. Modeling, discusses ways of using chaos theory to visualize the design process, a process which may be most resilient when it is nonlinear. The last research chapter, managing, applications of chaos theory are used to illustrate how complex form, like that in nature, can be created by designers. / Master of Landscape Architecture

139 leaves printed single pages, preliminary pages i-xi and numbered pages 1-127. Includes bibliography and a list of figures and tables. Digitized at 600 dpi grayscale to pdf format (OCR),using a Bizhub 250 Konica Minolta Scanner. / Thesis (MScEng (Electrical and Electronic Engineering))--University of Stellenbosch, 2003. / ENGLISH ABSTRACT: Probability density functions (PDFs) and cumulative distribution functions (CDFs) play a central role in statistical pattern recognition and verification systems. They allow observations that do not occur according to deterministic rules to be quantified and modelled. An example of such observations would be the voice patterns of a person that is used as input to a biometric security device. In order to model such non-deterministic observations, a density function estimator is employed to estimate a PDF or CDF from sample data. Although numerous density function estimation techniques exist, all the techniques can be classified into one of two groups, parametric and non-parametric, each with its own characteristic advantages and disadvantages. In this research, we introduce a novel approach to density function estimation that attempts to combine some of the advantages of both the parametric and non-parametric estimators. This is done by considering density estimation using an abstract approach in which the density function is modelled entirely in terms of its moments or characteristic function. New density function estimation techniques are first developed in theory, after which a number of practical density function estimators are presented. Experiments are performed in which the performance of the new estimators are compared to two established estimators, namely the Parzen estimator and the Gaussian mixture model (GMM). The comparison is performed in terms of the accuracy, computational requirements and ease of use of the estimators and it is found that the new estimators does combine some of the advantages of the established estimators without the corresponding disadvantages. / AFRIKAANSE OPSOMMING: Waarskynlikheids digtheidsfunksies (WDFs) en Kumulatiewe distribusiefunksies (KDFs) speel 'n sentrale rol in statistiese patroonherkenning en verifikasie stelsels. Hulle maak dit moontlik om nie-deterministiese observasies te kwantifiseer en te modelleer. Die stempatrone van 'n spreker wat as intree tot 'n biometriese sekuriteits stelsel gegee word, is 'n voorbeeld van so 'n observasie. Ten einde sulke observasies te modelleer, word 'n digtheidsfunksie afskatter gebruik om die WDF of KDF vanaf data monsters af te skat. Alhoewel daar talryke digtheidsfunksie afskatters bestaan, kan almal in een van twee katagoriee geplaas word, parametries en nie-parametries, elk met hul eie kenmerkende voordele en nadele. Hierdie werk Ie 'n nuwe benadering tot digtheidsfunksie afskatting voor wat die voordele van beide die parametriese sowel as die nie-parametriese tegnieke probeer kombineer. Dit word gedoen deur digtheidsfunksie afskatting vanuit 'n abstrakte oogpunt te benader waar die digtheidsfunksie uitsluitlik in terme van sy momente en karakteristieke funksie gemodelleer word. Nuwe metodes word eers in teorie ondersoek en ontwikkel waarna praktiese tegnieke voorgele word. Hierdie afskatters het die vermoe om 'n wye verskeidenheid digtheidsfunksies af te skat en is nie net ontwerp om slegs sekere families van digtheidsfunksies optimaal voor te stel nie. Eksperimente is uitgevoer wat die werkverrigting van die nuwe tegnieke met twee gevestigde tegnieke, naamlik die Parzen afskatter en die Gaussiese mengsel model (GMM), te vergelyk. Die werkverrigting word gemeet in terme van akkuraatheid, vereiste numeriese verwerkingsvermoe en die gemak van gebruik. Daar word bevind dat die nuwe afskatters weI voordele van die gevestigde afskatters kombineer sonder die gepaardgaande nadele.

The mirror neuron system consists of a specific class of visuomotor neurons, which fire for both observation and execution of an action (di Pellegrino et al., 1992), as well as showing differences for empathy and gender. Fifty males (M = 25.94) and fifty females (M = 25.48) watched short clips of a hand tapping fingers in a sequence in neutral and emotional settings. Participants were asked to imitate emotions while watching and repeating the finger sequences. A univariate ANOVA discovered significant differences in response times for males and females in the emotion trials, which were eliminated when empathy was included in the analysis. Findings show those higher in empathy are faster at imitation of a motor task in emotional settings. / Includes bibliography. / Thesis (M.A.)--Florida Atlantic University, 2014. / FAU Electronic Theses and Dissertations Collection